W47: Visualize global development
Magnus Thastum
29/11/2021
Explore global development with R
Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.
Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.
Get the necessary packages
First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.1.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()Look at the data and tackle the tasks
First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.
str(gapminder)## tibble [1,704 x 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...unique(gapminder$year)## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007head(gapminder)## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.
Let’s plot all the countries in 1952.
theme_set(theme_bw()) # set theme to white background for better visibility
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() 
…
We see an interesting spread with an outlier to the right. Answer the following questions, please:
- Why does it make sense to have a log10 scale on x axis?
By converting the x axis to a log10 scale the data becomes much easier to read. This is because a log10 graph is more precise closer to the y axis, which is because log10(10)=1, log10(100)=2, log10(1000)=3 etc. You can clearly se the benifits of convert to a log10 scale if you remove the function:
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() 
- Who is the outlier (the richest country in 1952 - far right on x axis)?
To find the outlier I made a pipe that would arrange the gdpPercap in 1952 in a decending order. So first I filtered the year and the arranged the results as follows:
gapminder %>%
filter(year==1952) %>%
arrange(desc(gdpPercap))## # A tibble: 142 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Kuwait Asia 1952 55.6 160000 108382.
## 2 Switzerland Europe 1952 69.6 4815000 14734.
## 3 United States Americas 1952 68.4 157553000 13990.
## 4 Canada Americas 1952 68.8 14785584 11367.
## 5 New Zealand Oceania 1952 69.4 1994794 10557.
## 6 Norway Europe 1952 72.7 3327728 10095.
## 7 Australia Oceania 1952 69.1 8691212 10040.
## 8 United Kingdom Europe 1952 69.2 50430000 9980.
## 9 Bahrain Asia 1952 50.9 120447 9867.
## 10 Denmark Europe 1952 70.8 4334000 9692.
## # ... with 132 more rowsThe outlier was Kuwait
Next, you can generate a similar plot for 2007 and compare the differences
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() 
…
The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.
Tasks:
- Differentiate the continents by color, and fix the axis labels and units to be more legible (Hint: the 2.50e+08 is so called “scientific notation”, which you might want to eliminate)
To defferentiate the continents i add the code: color=continent, the the aesthetic function (aes). Then I named the axis individually with xlab and ylab functions. The plot then looks as follows:
ggplot(subset(gapminder, year == 2007), aes(color=continent, gdpPercap, lifeExp, size = pop)) +
xlab("GDP per Capita") +
ylab("Life Expectancy") +
geom_point() +
scale_x_log10() 
- What are the five richest countries in the world in 2007?
To solve this question I again used the same type of pipe, as in question 2. The only difference is that I have filtered the year to 2007.
gapminder %>%
filter(year==2007) %>%
arrange(desc(gdpPercap))## # A tibble: 142 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Norway Europe 2007 80.2 4627926 49357.
## 2 Kuwait Asia 2007 77.6 2505559 47307.
## 3 Singapore Asia 2007 80.0 4553009 47143.
## 4 United States Americas 2007 78.2 301139947 42952.
## 5 Ireland Europe 2007 78.9 4109086 40676.
## 6 Hong Kong, China Asia 2007 82.2 6980412 39725.
## 7 Switzerland Europe 2007 81.7 7554661 37506.
## 8 Netherlands Europe 2007 79.8 16570613 36798.
## 9 Canada Americas 2007 80.7 33390141 36319.
## 10 Iceland Europe 2007 81.8 301931 36181.
## # ... with 132 more rowsIn the table you can see that the five richest countries in 2007 were Norway, Kuwait, Singapore, United states and Ireland
Make it move!
The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. Beware that there may be other packages your operating system needs in order to glue interim images into an animation or video. Read the messages when installing the package.
Also, there are two ways of animating the gapminder ggplot.
Option 1: Animate using transition_states()
The first step is to create the object-to-be-animated
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() # convert x to log scale
anim
…
This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the bottom right ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the visual inside an html file.
anim + transition_states(year,
transition_length = 1,
state_length = 1)
…
Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.
Option 2 Animate using transition_time()
This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() + # convert x to log scale
transition_time(year)
anim2
The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.
Now, choose one of the animation options and get it to work. You may need to troubleshoot your installation of gganimate and other packages
- Can you add a title to one or both of the animations above that will change in sync with the animation? (Hint: search labeling for
transition_states()andtransition_time()functions respectively)
I decited to add a title to option 2; the smooth animation. I did this by adding the line ‘labs(title = “Year: {round(frame_time,0)}”)’ to the plot
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() +
transition_time(year) +
labs(title = "Year: {round(frame_time,0)}")
anim2
- Can you made the axes’ labels and units more readable? Consider expanding the abreviated lables as well as the scientific notation in the legend and x axis to whole numbers.
I labeled the axis the same way i did privously, by addiing Xlab and ylab to the code
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() +
transition_time(year) +
xlab("GDP per Capita") +
ylab("Life Expectancy") +
labs(title = "Year: {round(frame_time,0)}")
anim2
- Come up with a question you want to answer using the gapminder data and write it down. Then, create a data visualisation that answers the question and explain how your visualization answers the question. (Example: you wish to see what was mean life expectancy across the continents in the year you were born versus your parents’ birth years). [Hint: if you wish to have more data than is in the filtered gapminder, you can load either the
gapminder_unfiltereddataset and download more at https://www.gapminder.org/data/ ]
My question is whether there are some continents that have experienced more development over the time period than others.
To answer this i coloured the gif by continent, to see if it could show anything, which it does. We can clearly see, in the gif below, that Asia developed at a far more rapid pace, leaving Africa as the undisputed ‘looser’ at the end of the animation. Though it should be mentioned that overall Africa also experienced positive development. So to answer the question: yes, we can see that there is some correlation between the different continents and their progress on the graph.
anim2 <- ggplot(gapminder, aes(color=continent, gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() +
transition_time(year) +
xlab("GDP per Capita") +
ylab("Life Expectancy") +
labs(title = "Year: {round(frame_time,0)}")
anim2